{"id":"https://openalex.org/W4413349693","doi":"https://doi.org/10.1109/icde65448.2025.00229","title":"Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering","display_name":"Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering","publication_year":2025,"publication_date":"2025-05-19","ids":{"openalex":"https://openalex.org/W4413349693","doi":"https://doi.org/10.1109/icde65448.2025.00229"},"language":"en","primary_location":{"id":"doi:10.1109/icde65448.2025.00229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014923511","display_name":"Yiming Niu","orcid":"https://orcid.org/0000-0001-9744-2339"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Niu","raw_affiliation_strings":["Beihang University,State Key Laboratory of Complex & Critical Software Environment, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,State Key Laboratory of Complex & Critical Software Environment, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000626453","display_name":"Jinliang Deng","orcid":"https://orcid.org/0000-0002-0759-947X"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]},{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Jinliang Deng","raw_affiliation_strings":["Hong Kong University of Science and Technology,HKGAI,Hong Kong SAR,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology,HKGAI,Hong Kong SAR,China","institution_ids":["https://openalex.org/I200769079","https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100431545","display_name":"Lulu Zhang","orcid":"https://orcid.org/0000-0001-9076-1898"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lulu Zhang","raw_affiliation_strings":["Beihang University,State Key Laboratory of Complex & Critical Software Environment, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,State Key Laboratory of Complex & Critical Software Environment, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing,Beijing,China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011140675","display_name":"Zimu Zhou","orcid":"https://orcid.org/0000-0002-5457-6967"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zimu Zhou","raw_affiliation_strings":["City University of Hong Kong,Department of Data Science,Hong Kong SAR,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Data Science,Hong Kong SAR,China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051874566","display_name":"Yongxin Tong","orcid":"https://orcid.org/0000-0002-5598-0312"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yongxin Tong","raw_affiliation_strings":["City University of Hong Kong,Department of Data Science,Hong Kong SAR,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of Hong Kong,Department of Data Science,Hong Kong SAR,China","institution_ids":["https://openalex.org/I168719708"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3056","last_page":"3069"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9894000291824341,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7527424097061157},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7309001088142395},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6776314973831177},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.646644115447998},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6280133724212646},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5226594805717468},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4965115189552307},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46798989176750183}],"concepts":[{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7527424097061157},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7309001088142395},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6776314973831177},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.646644115447998},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6280133724212646},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5226594805717468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4965115189552307},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46798989176750183},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icde65448.2025.00229","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icde65448.2025.00229","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 41st International Conference on Data Engineering (ICDE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/13"}],"awards":[{"id":"https://openalex.org/G8848731694","display_name":null,"funder_award_id":"62425202,U21A20516,62336003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1969852690","https://openalex.org/W2187089797","https://openalex.org/W2519091744","https://openalex.org/W2604847698","https://openalex.org/W2770058355","https://openalex.org/W2908510526","https://openalex.org/W2963870721","https://openalex.org/W2965341826","https://openalex.org/W2970658101","https://openalex.org/W3029687524","https://openalex.org/W3033529678","https://openalex.org/W3038981236","https://openalex.org/W3080253043","https://openalex.org/W3095487519","https://openalex.org/W3138851831","https://openalex.org/W3166508292","https://openalex.org/W3175924508","https://openalex.org/W3177318507","https://openalex.org/W3189124218","https://openalex.org/W3206797642","https://openalex.org/W3212890323","https://openalex.org/W4225862894","https://openalex.org/W4283721567","https://openalex.org/W4285357695","https://openalex.org/W4289533938","https://openalex.org/W4306317350","https://openalex.org/W4312713717","https://openalex.org/W4362655576","https://openalex.org/W4380433143","https://openalex.org/W4381326995","https://openalex.org/W4382202978","https://openalex.org/W4382203079","https://openalex.org/W4385245566","https://openalex.org/W4385270114","https://openalex.org/W4385288150","https://openalex.org/W4386768620","https://openalex.org/W4392397350","https://openalex.org/W4392453352","https://openalex.org/W4393924497","https://openalex.org/W4399114669","https://openalex.org/W4400909784","https://openalex.org/W4400910456","https://openalex.org/W4401353384","https://openalex.org/W4401864135","https://openalex.org/W4402042807","https://openalex.org/W4403577941","https://openalex.org/W4403600951"],"related_works":["https://openalex.org/W2406638334","https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W1991765889","https://openalex.org/W1990068454","https://openalex.org/W2472172556","https://openalex.org/W2119012848","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660"],"abstract_inverted_index":{"Accurate":[0],"and":[1,16,26,126,133,139],"efficient":[2,140],"multivariate":[3],"time":[4,43,111],"series":[5],"(MTS)":[6],"forecasting":[7,78],"is":[8],"essential":[9],"for":[10],"applications":[11],"such":[12],"as":[13],"traffic":[14],"management":[15],"weather":[17],"prediction,":[18],"which":[19],"depend":[20],"on":[21,35],"capturing":[22],"long-range":[23,81,157],"temporal":[24],"dependencies":[25,40,128,158],"interactions":[27],"between":[28,129],"entities.":[29],"Existing":[30],"methods,":[31],"particularly":[32],"those":[33],"based":[34],"Transformer":[36],"architectures,":[37],"compute":[38],"pairwise":[39],"across":[41,168],"all":[42],"steps,":[44],"leading":[45],"to":[46,76,122,163],"a":[47,73],"computational":[48,153,180],"complexity":[49,154],"that":[50,79,172],"scales":[51],"quadratically":[52],"with":[53,67],"the":[54,57,65,85,99,103,106,114,123,130,146,152,160],"length":[55],"of":[56,87,109,155],"input.":[58],"To":[59],"overcome":[60],"these":[61,120],"challenges,":[62],"we":[63],"introduce":[64],"Forecaster":[66],"Offline":[68],"Clustering":[69],"Using":[70],"Segments":[71],"(FOCUS),":[72],"novel":[74],"approach":[75],"MTS":[77],"simplifies":[80],"dependency":[82],"modeling":[83,156],"through":[84],"use":[86],"prototypes":[88,94,144],"extracted":[89],"via":[90],"offline":[91,147],"clustering.":[92],"These":[93],"encapsulate":[95],"high-level":[96,134],"events":[97],"in":[98,159],"real-world":[100],"system":[101],"underlying":[102],"data,":[104],"summarizing":[105],"key":[107],"characteristics":[108],"similar":[110],"segments.":[112],"In":[113],"online":[115,161],"phase,":[116,149],"FOCUS":[117,150,173],"dynamically":[118],"adapts":[119],"patterns":[121],"current":[124],"input":[125,131],"captures":[127],"segment":[132],"events,":[135],"enabling":[136],"both":[137],"accurate":[138],"forecasting.":[141],"By":[142],"identifying":[143],"during":[145],"clustering":[148],"reduces":[151],"phase":[162],"linear":[164],"scaling.":[165],"Extensive":[166],"experiments":[167],"diverse":[169],"benchmarks":[170],"demonstrate":[171],"achieves":[174],"state-of-the-art":[175],"accuracy":[176],"while":[177],"significantly":[178],"reducing":[179],"costs.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}